There’s a lot of noise right now about how generative AIs like ChatGPT and Bard are going to revolutionize various aspects of the web, but companies targeting narrower verticals are already experiencing success. Writer is such a one, and it just announced a new trio of large language models to power its enterprise copy assistant.
The company lets customers fine-tune these models on their own content and style guides, from which point forward the AI can write, help write, or edit copy so that it meets internal standards. More than just catching typos and recommending the preferred word, Writer’s new models can evaluate style and write content themselves, even doing a bit of fact-checking when they’re done.
But the real draw is that the whole thing can be done internally, from fine-tuning to hosting, at least when it comes to the smaller two of the Palmyra series of models.
“No enterprise leader wants their data to be fodder for someone else’s foundation model, including ours,” said CEO May Habib in a press release. “We give customers all the benefits of the AI application layer without any of the risks of other AI applications and commercial models. Enterprise leaders want to invest in solutions that will essentially give them their own LLM.”
Palymra comes in three sizes: 128 million, 5 billion, or 20 billion parameters respectively for Small, Base, and Large. They’re trained on business and marketing writing, not Reddit posts and Project Gutenberg, so there are less surprises to begin with. Then you load up its maw with the last ten years of annual reports, financials, blog posts, and so on to make it yours. (This and any derived data do not filter back to Writer, to be clear.)
Having written my share of enterprise and marketing copy, I can say this isn’t the most exciting of applications. But what it lacks in thrills it makes up for in practicality: companies need to do lots of this kind of writing and editing, and tend to actually pay for it. Writer already hooks into lots of development and productivity suites, so there’s not much friction added.
The business model is similar to other generative AI companies: you get all set up and fine-tuned for free, then pay a penny per thousand tokens, which gets you about 750 words. (This article is just over 500, as a quick reference.)
Alternatively, you can self-hose the Small or Base models free of charge if you have the compute.
A few dozen companies have been using the models since late last year, and we haven’t heard about any egregious problems like we did on day one of Microsoft and Google’s attempts at popularizing generative AI… so that’s a good sign. This is the success of which I spoke earlier. While ChatGPT is certainly impressive, as a generalist or dilettante AI it’s hard to say what it’s actually capable of being used for. The next year or two will see more targeted plays like Writers while Microsoft and Google kick the tires on their latest toy.
Tesla more than tripled its Austin gigafactory workforce in 2022
Tesla’s 2,500-acre manufacturing hub in Austin, Texas tripled its workforce last year, according to the company’s annual compliance report filed with county officials. Bloomberg first reported on the news.
The report filed with Travis County’s Economic Development Program shows that Tesla increased its Austin workforce from just 3,523 contingent and permanent employees in 2021 to 12,277 by the end of 2022. Bloomberg reports that just over half of Tesla’s workers reside in the county, with the average full-time employee earning a salary of at least $47,147. Outside of Tesla’s factory, the average salary of an Austin worker is $68,060, according to data from ZipRecruiter.
TechCrunch was unable to acquire a copy of the report, so it’s not clear if those workers are all full-time. If they are, Tesla has hired a far cry more full-time employees than it is contracted to do. According to the agreement between Tesla and Travis County, the company is obligated to create 5,001 new full-time jobs over the next four years.
The contract also states that Tesla must invest about $1.1 billion in the county over the next five years. Tesla’s compliance report shows that the automaker last year invested $5.81 billion in Gigafactory Texas, which officially launched a year ago at a “Cyber Rodeo” event. In January, Tesla notified regulators that it plans to invest another $770 million into an expansion of the factory to include a battery cell testing site and cathode and drive unit manufacturing site. With that investment will come more jobs.
Tesla’s choice to move its headquarters to Texas and build a gigafactory there has helped the state lead the nation in job growth. The automaker builds its Model Y crossover there and plans to build its Cybertruck in Texas, as well. Giga Texas will also be a model for sustainable manufacturing, CEO Elon Musk has said. Last year, Tesla completed the first phase of what will become “the largest rooftop solar installation in the world,” according to the report, per Bloomberg. Tesla has begun on the second phase of installation, but already there are reports of being able to see the rooftop from space. The goal is to generate 27 megawatts of power.
Musk has also promised to turn the site into an “ecological paradise,” complete with a boardwalk and a hiking/biking trail that will open to the public. There haven’t been many updates on that front, and locals have been concerned that the site is actually more of an environmental nightmare that has led to noise and water pollution. The site, located at the intersection of State Highway 130 and Harold Green Road, east of Austin, is along the Colorado River and could create a climate catastrophe if the river overflows.
The site of Tesla’s gigafactory has also historically been the home of low-income households and has a large population of Spanish-speaking residents. It’s not clear if the jobs at the factory reflect the demographic population of the community in which it resides.
Launch startup Stoke Space rolls out software tool for complex hardware development
Stoke Space, a company that’s developing a fully reusable rocket, has unveiled a new tool to let hardware companies track the design, testing and integration of parts. The new tool, Fusion, is targeting an unsexy but essential aspect of the hardware workflow.
It’s a solution born out of “ubiquitous pain in the industry,” Stoke CEO Andy Lapsa said in a recent interview. The current parts tracking status quo is marked by cumbersome, balkanized solutions built on piles of paperwork and spreadsheets. Many of the existing tools are not optimized “for boots on the ground,” but for finance or procurement teams, or even the C-suite, Lapsa explained.
In contrast, Fusion is designed to optimize simple inventory transactions and parts organization, and it will continue to track parts through their lifespan: as they are built into larger assemblies and go through testing. In an extreme example, such as hardware failures, Fusion will help teams connect anomalous data to the exact serial numbers of the parts involved.
“If you think about aerospace in general, there’s a need and a desire to be able to understand the part pedigree of every single part number and serial number that’s in an assembly,” Lapsa said. “So not only do you understand the configuration, you understand the history of all of those parts dating back to forever.”
While Lapsa clarified that Fusion is the result of an organic in-house need for better parts management – designing a fully reusable rocket is complicated, after all – turning it into a sell-able product was a decision that the Stoke team made early on. It’s a notable example of a rocket startup generating pathways for revenue while their vehicle is still under development.
Fusion offers particular relevance to startups. Many existing tools are designed for production runs – not the fast-moving research and development environment that many hardware startups find themselves, Lapsa added. In these environments, speed and accuracy are paramount.
Brent Bradbury, Stoke’s head of software, echoed these comments.
“The parts are changing, the people are changing, the processes are changing,” he said. “This lets us capture all that as it happens without a whole lot of extra work.”
Amid a boom in AI accelerators, a UC Berkeley-focused outfit, House Fund, swings open its doors
Companies at the forefront of AI would naturally like to stay at the forefront, so it’s no surprise they want to stay close to smaller startups that are putting some of their newest advancements to work.
Last month, for example, Neo, a startup accelerator founded by Silicon Valley investor Ali Partovi, announced that OpenAI and Microsoft have offered to provide free software and advice to companies in a new track focused on artificial intelligence.
Now, another Bay Area outfit — House Fund, which invests in startups with ties to UC Berkeley — says it is launching an AI accelerator and that, similarly, OpenAI, Microsoft, Databricks, and Google’s Gradient Ventures are offering participating startups free and early access to tech from their companies, along with mentorship from top AI founders and executives at these companies.
We talked with House Fund founder Jeremy Fiance over the weekend to get a bit more color about the program, which will replace a broader-based accelerator program House Fund has run and whose alums include an additive manufacturing software company, Dyndrite, and the managed app development platform Chowbotics, whose most recent round in January brought the company’s total funding to more than $60 million.
For founders interested in learning more, the new AI accelerator program runs for two months, kicking off in early July and ending in early September. Six or so companies will be accepted, with the early application deadline coming up next week on April 13th. (The final application deadline is on June 1.) As for the time commitment involved across those two months, every startup could have a different experience, says Fiance. “We’re there when you need us, and we’re good at staying out of the way.”
There will be the requisite kickoff retreat to spark the program and founders to get to know one another. Candidates who are accepted will also have access to some of UC Berkeley’s renowned AI professors, including Michael Jordan, Ion Stoica, and Trevor Darrell. And they can opt into dinners and events in collaboration with these various constituents.
As for some of the financial dynamics, every startup that goes through the program will receive a $1 million investment on a $10 million post-money SAFE note. Importantly, too, as with the House Fund’s venture dollars, its AI accelerator is seeking startups that have at least one Berkeley-affiliated founder on the co-founding team. That includes alumni, faculty, PhDs, postdocs, staff, students, dropouts, and other affiliates.
There is no demo day. Instead, says Fiance, founders will receive “directed, personal introductions” to the VCs who best fit with their startups.
Given the buzz over AI, the new program could supercharge House Fund, the venture organization, which is already growing fast. Fiance launched it in 2016 with just $6 million and it now manages $300 million in assets, including on behalf of Berkeley Endowment Management Company and the University of California.
At the same time, the competition out there is fierce and growing more so by the day.
Though OpenAI has offered to partner with House Fund, for example, the San Francisco-based company announced its own accelerator back in November. Called Converge, the cohort was to be made up of 10 or so founders who received $1 million each and admission to five weeks of office hours, workshops and other events that ended and that received their funding from the OpenAI Startup Fund.
Y Combinator, the biggest accelerator in the world, is also oozing with AI startups right now, all of them part of a winter class that will be talking directly with investors this week via demo days that are taking place tomorrow, April 5th, and on Thursday.
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